Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study
Abstract Background Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitate early intervention and prevention of lun...
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BMC
2025-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13562-w |
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| author | Songjing Chen Sizhu Wu |
| author_facet | Songjing Chen Sizhu Wu |
| author_sort | Songjing Chen |
| collection | DOAJ |
| description | Abstract Background Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitate early intervention and prevention of lung cancer. Methods We stratified the population into six subgroups according to age and gender. For each subgroup, random forest, extreme gradient boosting, deep neural networks, support vector machine, multiple logistic regression and deep Q network (DQN) models were developed and validated. Models were trained and tested using samples from 2000 to 2015 and independent external validated through those from 2016 to 2019. The suitable model for lung cancer risk prediction and high risk factors identification was chosen based on internal validation and independent external validation. Results The DQN model achieved the optimal prediction performance in stratified subgroups, with AUROC ranging from 0.937 to 0.953, recall ranging from 0.932 to 0.943, F 2 -score ranging from 0.929 to 0.946, precision ranging from 0.926 to 0.952, F 1 -score ranging from 0.933 to 0.963 and RMSE ranging from 0.21 to 0.27. SHAP values were supplied for model interpretability. High risk factors of lung cancer incidence were identified in the elderly. Men ≥ 65 carrying C > A/G > T mutation had the highest lung cancer incidence decrease of 39.5% after five years quitting in stratified elderly groups, which were 1.83 times more than women ≥ 65 not carrying C > A/G > T mutation. Conclusions The DQN model may be suitable for identifying high risk factors and predicting lung cancer risk with high performance. The proposed intervention and diagnosis pathways could be used for early screening and intervention before the occurrence of lung cancer, which could help oncologists develop targeted intervention strategies for the stratified elderly to reduce lung cancer incidence and improve therapeutic effect. Proposed method could also be used in predicting the risk of other chronic diseases to help conduct intervention and reduce incidence. |
| format | Article |
| id | doaj-art-892031a8ecf94c8fa613cfe213fe5d93 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-892031a8ecf94c8fa613cfe213fe5d932025-01-26T12:37:50ZengBMCBMC Cancer1471-24072025-01-0125111410.1186/s12885-025-13562-wEnsemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal studySongjing Chen0Sizhu Wu1Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical CollegeInstitute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical CollegeAbstract Background Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitate early intervention and prevention of lung cancer. Methods We stratified the population into six subgroups according to age and gender. For each subgroup, random forest, extreme gradient boosting, deep neural networks, support vector machine, multiple logistic regression and deep Q network (DQN) models were developed and validated. Models were trained and tested using samples from 2000 to 2015 and independent external validated through those from 2016 to 2019. The suitable model for lung cancer risk prediction and high risk factors identification was chosen based on internal validation and independent external validation. Results The DQN model achieved the optimal prediction performance in stratified subgroups, with AUROC ranging from 0.937 to 0.953, recall ranging from 0.932 to 0.943, F 2 -score ranging from 0.929 to 0.946, precision ranging from 0.926 to 0.952, F 1 -score ranging from 0.933 to 0.963 and RMSE ranging from 0.21 to 0.27. SHAP values were supplied for model interpretability. High risk factors of lung cancer incidence were identified in the elderly. Men ≥ 65 carrying C > A/G > T mutation had the highest lung cancer incidence decrease of 39.5% after five years quitting in stratified elderly groups, which were 1.83 times more than women ≥ 65 not carrying C > A/G > T mutation. Conclusions The DQN model may be suitable for identifying high risk factors and predicting lung cancer risk with high performance. The proposed intervention and diagnosis pathways could be used for early screening and intervention before the occurrence of lung cancer, which could help oncologists develop targeted intervention strategies for the stratified elderly to reduce lung cancer incidence and improve therapeutic effect. Proposed method could also be used in predicting the risk of other chronic diseases to help conduct intervention and reduce incidence.https://doi.org/10.1186/s12885-025-13562-wLung cancerIncidence riskElderlyMachine learningPrediction |
| spellingShingle | Songjing Chen Sizhu Wu Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study BMC Cancer Lung cancer Incidence risk Elderly Machine learning Prediction |
| title | Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study |
| title_full | Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study |
| title_fullStr | Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study |
| title_full_unstemmed | Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study |
| title_short | Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study |
| title_sort | ensemble machine learning models for lung cancer incidence risk prediction in the elderly a retrospective longitudinal study |
| topic | Lung cancer Incidence risk Elderly Machine learning Prediction |
| url | https://doi.org/10.1186/s12885-025-13562-w |
| work_keys_str_mv | AT songjingchen ensemblemachinelearningmodelsforlungcancerincidenceriskpredictionintheelderlyaretrospectivelongitudinalstudy AT sizhuwu ensemblemachinelearningmodelsforlungcancerincidenceriskpredictionintheelderlyaretrospectivelongitudinalstudy |